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We develop a phase-field model of brittle fracture to model fracture in sea ice floes. Phase fields allow for a variational formulation of fracture by using an energy functional that combines a linear elastic energy with a term modeling the energetic cost of fracture. We study the fracture strength of ice floes with stochastic thickness variations under boundary forcings or displacements. Our approach models refrozen cracks or other linear ice impurities with stochastic models for thickness profiles. We find that the orientation of thickness variations is an important factor for the strength of ice floes, and we study the distribution of critical stresses leading to fracture. Potential applications to discrete element method (DEM) simulations and field data from the ICEX 2018 campaign are discussed.

Induction of meiotic and post-meiotic alterations of male germ cells in vitro has been the target of several research efforts since 1960. However, to date, the establishment of an ideal culture system in which spermatogonial stem cells can be maintained and directed to proliferate and undergo meiosis and complete spermiogenesis does not exist. This is attributed to the difficulties concerning the isolation and purification of defined subpopulations of germ cells and the establishment of male germ cell lines. In addition, there is no adequate knowledge regarding the optimal biochemical conditions that promote the survival and differentiation of germ cells in long-term cultures. This review focuses on the methodologies that have been proved sufficient to achieve differentiation of cultured male germ cells. Furthermore, the factors regulating spermatogenesis and the technical prerequisites to achieve differentiation of cultured male germ cells are described. Finally, the role of in vitro cultures of immature diploid germ cells in the therapeutic management of men negative for haploid cells in their testes and the subsequent potential genetic and epigenetic risks are discussed. [ABSTRACT FROM AUTHOR]

Results from the transplantation of donor male germ cells into xenogeneic recipient seminiferous tubules indicate that donor spermatogonia are capable of differentiating to form spermatozoa morphologically characteristic of the donor species. Germ cell transplantation procedures combined with developments in freezing, culturing or enriching germ cell populations have applications of paramount importance in medicine, basic sciences and animal reproduction. Additionally, these techniques can serve as an alternative approach for gonadal protection and fertility preservation in patients with cancer. This article is a chronological critical review of the technological advances that followed the initial successful transplantation of mouse germ cells into recipient mice. Furthermore, the factors responsible for the immunological privilege properties of the testis and the parameters influencing the potential of mammalian germ cells to undergo mitosis and meiosis within a xenogeneic testis are described. Finally, the role of human germ cell transplantation procedures in the therapeutic management of non-obstructive azoospermia is discussed. [ABSTRACT FROM AUTHOR]

An important problem in modern applied science is to characterize the behavior of systems with complex internal dynamics subjected to external forcings. Many existing approaches rely on ensembles to generate information from the external forcings, making them unsuitable to study natural systems where only a single realization is observed. A prominent example is climate dynamics, where an objective identification of signals in the observational record attributable to natural variability and climate change is crucial for making climate projections for the coming decades. Here, we show that operator-theoretic techniques previously developed to identify slowly decorrelating observables of autonomous dynamical systems provide a powerful means for identifying nonlinear trends and persistent cycles of non-autonomous systems using data from a single trajectory of the system. We apply our framework to real-world examples from climate dynamics: Variability of sea surface temperature over the industrial era and the mid-Pleistocene transition of Quaternary glaciation cycles., (© 2024. The Author(s).)

The data on tumor molecular profiling of European patients with prostate cancer is limited. Our aim was to evaluate the prevalence and prognostic and predictive values of gene alterations in unselected patients with prostate cancer. The presence of gene alterations was assessed in patients with histologically confirmed prostate cancer using the ForeSENTIA ® Prostate panel (Medicover Genetics), targeting 36 clinically relevant genes and microsatellite instability testing. The primary endpoint was the prevalence of gene alterations in homologous recombination repair (HRR) genes. Overall, 196 patients with prostate cancer were evaluated (median age 72.2 years, metastatic disease in 141 (71.9%) patients). Gene alterations were identified in 120 (61%) patients, while alteration in HRR genes were identified in 34 (17.3%) patients. The most commonly mutated HRR genes were ATM (17, 8.7%), BRCA2 (9, 4.6%) and BRCA1 (4, 2%). The presence of HRR gene alterations was not associated with advanced stage ( p = 0.21), age at diagnosis ( p = 0.28), Gleason score ( p = 0.17) or overall survival (HR 0.72; 95% CI: 0.41-1.26; p = 0.251). We identified clinically relevant somatic gene alterations in European patients with prostate cancer. These molecular alterations have prognostic significance and therapeutic implications and/or may trigger genetic testing in selected patients. In the era of precision medicine, prospective research on the predictive role of these alterations for innovative treatments or their combinations is warranted.

We develop an algebraic framework for sequential data assimilation of partially observed dynamical systems. In this framework, Bayesian data assimilation is embedded in a nonabelian operator algebra, which provides a representation of observables by multiplication operators and probability densities by density operators (quantum states). In the algebraic approach, the forecast step of data assimilation is represented by a quantum operation induced by the Koopman operator of the dynamical system. Moreover, the analysis step is described by a quantum effect, which generalizes the Bayesian observational update rule. Projecting this formulation to finite-dimensional matrix algebras leads to computational schemes that are i) automatically positivity-preserving and ii) amenable to consistent data-driven approximation using kernel methods for machine learning. Moreover, these methods are natural candidates for implementation on quantum computers. Applications to the Lorenz 96 multiscale system and the El Niño Southern Oscillation in a climate model show promising results in terms of forecast skill and uncertainty quantification.

The Earth's climate system is a classical example of a multiscale, multiphysics dynamical system with an extremely large number of active degrees of freedom, exhibiting variability on scales ranging from micrometers and seconds in cloud microphysics, to thousands of kilometers and centuries in ocean dynamics. Yet, despite this dynamical complexity, climate dynamics is known to exhibit coherent modes of variability. A primary example is the El Niño Southern Oscillation (ENSO), the dominant mode of interannual (3-5 yr) variability in the climate system. The objective and robust characterization of this and other important phenomena presents a long-standing challenge in Earth system science, the resolution of which would lead to improved scientific understanding and prediction of climate dynamics, as well as assessment of their impacts on human and natural systems. Here, we show that the spectral theory of dynamical systems, combined with techniques from data science, provides an effective means for extracting coherent modes of climate variability from high-dimensional model and observational data, requiring no frequency prefiltering, but recovering multiple timescales and their interactions. Lifecycle composites of ENSO are shown to improve upon results from conventional indices in terms of dynamical consistency and physical interpretability. In addition, the role of combination modes between ENSO and the annual cycle in ENSO diversity is elucidated., (© 2021. The Author(s).)

In this study, natural zeolite with maximum adsorption capacity of 3.59 mg g -1 was used for the simultaneous removal of ammonium nitrogen (NH 4 -N), dissolved chemical oxygen demand (d-COD) and color from raw sanitary landfill leachate (SLL). Saturation, desorption and regeneration tests of zeolite were performed. Optimum adsorption conditions were found for particle size 0.930 µm, stirring rate of 1.18 m s + -N), dissolved chemical oxygen demand (d-COD) and color from raw sanitary landfill leachate (SLL). Saturation, desorption and regeneration tests of zeolite were performed. Optimum adsorption conditions were found for particle size 0.930 µm, stirring rate of 1.18 m s -1 , zeolite dosage of 133 g L -1 and pH 8. NH 4 + -N removal efficiency reached 51.63 ± 0.80% within 2.5 min of contact. NH 4 + -N adsorption follows mostly the linear pseudo-second order model, with intra-particle diffusion. NH 4 + -N desorption follows the linear pseudo-second order model. Adsorption data fitted to the Temkin Isotherm in linear and nonlinear forms. Saturation tests showed that zeolite can be efficiently used in three successive adsorption cycles. NH 4 + -N release from the saturated zeolite was not completely reversible, suggesting that the zeolite may be used as slow ΝΗ 4 + -Ν releasing fertilizer and an attractive low cost material for the treatment of SLL. NH 4 + -N removal with the regenerated zeolite exceeded 40% of the initial concentration in the fluid within 2.5 min. SEM analysis showed significant changes through saturation and regeneration. XPS revealed that adsorption of ΝΗ 4 + -Ν to the zeolite was accompanied by ion exchange., (Copyright © 2020 Elsevier B.V. All rights reserved.)

Forecasting the El Niño-Southern Oscillation (ENSO) has been a subject of vigorous research due to the important role of the phenomenon in climate dynamics and its worldwide socioeconomic impacts. Over the past decades, numerous models for ENSO prediction have been developed, among which statistical models approximating ENSO evolution by linear dynamics have received significant attention owing to their simplicity and comparable forecast skill to first-principles models at short lead times. Yet, due to highly nonlinear and chaotic dynamics (particularly during ENSO initiation), such models have limited skill for longer-term forecasts beyond half a year. To resolve this limitation, here we employ a new nonparametric statistical approach based on analog forecasting, called kernel analog forecasting (KAF), which avoids assumptions on the underlying dynamics through the use of nonlinear kernel methods for machine learning and dimension reduction of high-dimensional datasets. Through a rigorous connection with Koopman operator theory for dynamical systems, KAF yields statistically optimal predictions of future ENSO states as conditional expectations, given noisy and potentially incomplete data at forecast initialization. Here, using industrial-era Indo-Pacific sea surface temperature (SST) as training data, the method is shown to successfully predict the Niño 3.4 index in a 1998-2017 verification period out to a 10-month lead, which corresponds to an increase of 3-8 months (depending on the decade) over a benchmark linear inverse model (LIM), while significantly improving upon the ENSO predictability "spring barrier". In particular, KAF successfully predicts the historic 2015/16 El Niño at initialization times as early as June 2015, which is comparable to the skill of current dynamical models. An analysis of a 1300-yr control integration of a comprehensive climate model (CCSM4) further demonstrates that the enhanced predictability afforded by KAF holds over potentially much longer leads, extending to 24 months versus 18 months in the benchmark LIM. Probabilistic forecasts for the occurrence of El Niño/La Niña events are also performed and assessed via information-theoretic metrics, showing an improvement of skill over LIM approaches, thus opening an avenue for environmental risk assessment relevant in a variety of contexts.

A framework for data assimilation combining aspects of operator-theoretic ergodic theory and quantum mechanics is developed. This framework adapts the Dirac-von Neumann formalism of quantum dynamics and measurement to perform sequential data assimilation (filtering) of a partially observed, measure-preserving dynamical system, using the Koopman operator on the L^{2} space associated with the invariant measure as an analog of the Heisenberg evolution operator in quantum mechanics. In addition, the state of the data assimilation system is represented by a trace-class operator analogous to the quantum mechanical density operator, and the assimilated observables by self-adjoint multiplication operators. An averaging approach is also introduced, rendering the spectrum of the assimilated observables discrete and thus amenable to numerical approximation. We present a data-driven formulation of the quantum mechanical data assimilation approach, utilizing kernel methods from machine learning and delay-coordinate maps of dynamical systems to represent the evolution and measurement operators via matrices in a data-driven basis. The data-driven formulation is structurally similar to its infinite-dimensional counterpart and shown to converge in a limit of large data under mild assumptions. Applications to periodic oscillators and the Lorenz 63 system demonstrate that the framework is able to naturally handle highly non-Gaussian statistics, complex state space geometries, and chaotic dynamics.

Understanding chemical mechanisms requires estimating dynamical statistics such as expected hitting times, reaction rates, and committors. Here, we present a general framework for calculating these dynamical quantities by approximating boundary value problems using dynamical operators with a Galerkin expansion. A specific choice of basis set in the expansion corresponds to the estimation of dynamical quantities using a Markov state model. More generally, the boundary conditions impose restrictions on the choice of basis sets. We demonstrate how an alternative basis can be constructed using ideas from diffusion maps. In our numerical experiments, this basis gives results of comparable or better accuracy to Markov state models. Additionally, we show that delay embedding can reduce the information lost when projecting the system's dynamics for model construction; this improves estimates of dynamical statistics considerably over the standard practice of increasing the lag time.

Objectives: The objective of this study was to test platelet function pre- and peri-operatively in clopidogrel-treated patients undergoing transurethral resection of the prostate., Methods: This was a pilot study involving 20 male patients treated with clopidogrel (75 mg/day) for the secondary prevention of cardiovascular disease and scheduled for elective transurethral resection of the prostate. Platelet function testing with light transmittance aggregometry in platelet-rich plasma of four samples (T0, T1, T2, and T3 drawn on the same day, 3 and 7 days of clopidogrel cessation and 24-h post-operatively, respectively) was performed and evaluated in each patient. P-selectin membrane expression was evaluated using monoclonal antibodies., Results: The platelet response to adenosine diphosphate 5 µΜ and 20 µΜ at T0 were 42 ± 15 and 60 ± 14%, respectively. After discontinuation of clopidogrel, corresponding maximum aggregation values at T1 were 60 ± 16 and 74 ± 14%, and increased to 69 ± 16 and 79 ± 18% at T2. No significant difference in platelet aggregation values were noted between T1 and T2, while similar aggregation values were recorded at T3., Conclusions: Our findings indicate that in patients undergoing transurethral resection of the prostate, platelet activation is similar 3 and 7 days from clopidogrel cessation. These results may be of relevance in subjects at increased thrombotic risk prior to a surgical procedure carrying a high-bleeding risk.